PROSTATEx Challenge (November 21, 2016 to February 16, 2017)

SPIE, along with the support of the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI), conducted a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of clinically significant prostate lesions.  For more details, go to https://prostatex.grand-challenge.org/ .

Training and test cohorts along with supplemental Ktrans images and lesion information can be obtained separately using the following options for download:

The images come in two encodings. The acquired MR is provided in DICOM encoding. Additionally Ktrans images are provided. They come in mhd format. Ktrans is a key pharmacokinetic parameter computed from the available Dynamic contrast enhanced T1-weighted series. Each patient has one study with several DICOM images and one Ktrans image. The Ktrans image is encoded in two files ProstateX-[ProxID]-Ktrans.[mhd/zraw], where ProxID is the ProstateX patient identifier. The DICOM images comprise several Series each comprising several Instances. The DICOM files are documented in the ProstateX-Images.csv file. The columns in that file encode the following:

• ProxID – ProstateX patient identifier.
• Name – Series Description
• Studydate – Study Date
• fid – Finding ID
• Pos – Scanner Coordinate position of the finding
• WorldMatrix – Matrix describing image orientation and scaling
• ijk – image col,row,slice coordinate of finding
• ImageUID – Image Identifier
• TopLevel
  • 0 – Series forms one image
  • 1 – A set of Series forms a 4D image (e.g. Dynamic MR)
  • NA – Series form one image, but is part of a Level 1 4D image
• SpacingBetweenSlices – Scalar Spacing between slices
• VoxelSpacing – Vector with x,y,z spacing scalars
• Dim – Vector with 4D dimensions of the image
• DCMSerDescr – The original DICOM Series Description
• DCMSerUID – The DICOM Series UID
• DCMSerNum – The DICOM Series Number
• InstanceUIDList – DICOM Instances that make up this series
• ImageUIDList – TopLevel-NA Images the make up this Toplevel 1 image

For example, to get the ADC image of Patient ProstateX-0123 do the following. After you imported the DICOM files into your environment, go to patient ProstateX-0123 and find the series with ADC in it. In this case it is ‘ep2d_diff_tra_DYNDIST_ADC’. It has SeriesNumber 8. The DICOM images in that series form the ADC image for this challenge. Image slice j at coordinate i,j contains a finding fid. See Findings for more details.

Findings

The findings are documented in the ProstateX-Findings.csv table. Documentation for the columns in that table is as follows:

• ProxID – ProstateX patient identifier

• fid – Finding ID

• pos – Scanner Coordinate position of the finding
• ClinSig – Identifier available in training set that identifies whether this is a clinically significant finding. Either the biopsy GleasonScore was 7 or higher. Findings with a PIRADS score 2 were not biopsied and are not considered clinically significant. In our center the occurrence of clinically significant cancer in PIRADS 2 lesions is less than 5%.

Note, ProxID is the PROSTATEx case ID, and fid is the finding (i.e., lesion) ID in both the ProstateX-Findings.csv file and the ProstateX-Images.csv file.  The Findings spreadsheet has one row per lesion (if a case has only one lesion, then the only fid for that case will be “1” , alternately if a case has two lesions, then there will be an fid of “1” and an fid of “2” for that case).  The Images spreadsheet has a row for every image that contains the lesion – that is why there are multiple rows with the same (ProxID, fid) combination.

PROSTATEx-2 — SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge (May 15, 2017 to August 3, 2017)

The American Association of Physicists in Medicine (AAPM), along with the SPIE (the international society for optics and photonics) and the National Cancer Institute (NCI), conducted a part 2 “Grand Challenge” on the development of quantitative multi-parametric magnetic resonance imaging (MRI) biomarkers for the determination of Gleason Grade Group in prostate cancer. For more details about PROSTATEx-2 please go to http://www.aapm.org/GrandChallenge/PROSTATEx-2/default.asp.

Training and test cohorts along with supplemental Ktrans images and lesion information can be obtained separately using the following options for download:

The training set will consist of 112 findings. This dataset will be representative of the technical properties (scanner type, acquisition parameters, file format) and the nature of the prostate lesions of the test set. An associated Excel file will include case name, the coordinates of the centroid of all lesions, and the lesion ‘truth’ label (Gleason Grade Group). Reference thumbnail images of the lesions will also be provided.

The test set will consist of 70 findings. The locations of the lesions will be specified in the accompanying Excel file that will follow the same format as for the training set with the omission of the ‘truth’ labels. Reference thumbnail images of the lesions will also be provided.

Publications by the Dataset Authors

The authors recommended this paper as the best source of additional information about this dataset:

• Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., & Huisman, H. (2014). Computer-Aided Detection of Prostate Cancer in MRI. In IEEE Transactions on Medical Imaging (Vol. 33, Issue 5, pp. 1083–1092). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tmi.2014.2303821

No publications by dataset authors were found.

Research Community Publications

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Version 1: Updated 2017/03/29